Real-time estimation of local cardiac tissue properties and uncertainties based on imaging and electro-anatomical maps

ABSTRACT

Systems and methods for automatically detecting a disease in medical images are provided. Input medical images are received. A plurality of metrics for a disease is computed for each of the input medical images. The input medical images are clustered into a plurality of clusters based on one or more of the plurality of metrics to classify the input medical images. The plurality of clusters comprise a cluster of one or more of the input medical images associated with the disease and one or more clusters of one or more of the input medical images not associated with the disease. In one embodiment, the disease is COVID-19 (coronavirus disease 2019).

CROSS-REFERENCE TO RELATED APPLICATIONS

Certain embodiments described herein may be related to U.S. patentapplication Ser. No. 16/837,979, filed Apr. 1, 2020, the disclosure ofwhich is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates generally to automatic detection ofCOVID-19 (coronavirus disease 2019) in chest CT (computed tomography)images, and in particular to automatic classification of chest CT imagesto distinguish COVID-19 from other pulmonary diseases using machinelearning.

BACKGROUND

COVID-19 (coronavirus disease 2019) is an infectious disease caused bythe severe-acute respiratory symptom coronavirus 2 (SARS-Cov2). COVID-19presents such respiratory symptoms as coughing, difficulty breathing,pneumonia, and SARS (severe acute respiratory syndrome). In the currentclinical practice, COVID-19 is diagnosed via RT-PCR (reversetranscription polymerase chain reaction).

Typically, a patient suspect of, or confirmed as, having COVID-19receives CT imaging of the chest to evaluate the lungs of the patient.Recently, techniques have been proposed for detecting COVID-19 in CTimages. However, it is not clear whether conventional techniques areable to distinguish CT images of COVID-19 not only from CT images ofhealthy patients, but also from CT images of other pulmonary disease,such as other infections, malignancy, ILD (interstitial lung disease),and COPD (chronic obstructive pulmonary disease). This is especiallyimportant as COVID-19 can manifest similarly to other pulmonarydiseases, which can lead to confusion in triage and diagnosis. Inaddition, some conventional techniques have been developed with limitedgeneralizability, while other conventional techniques do not providedetails, such as acquisition protocols or geographic location of origin,on the imaging data from which the techniques were developed.

BRIEF SUMMARY OF THE INVENTION

In accordance with one or more embodiments, systems and methods forautomatically detecting a disease in medical images are provided. Inputmedical images are received. A plurality of metrics for a disease iscomputed for each of the input medical images. The input medical imagesare clustered into a plurality of clusters based on one or more of theplurality of metrics to classify the input medical images. The pluralityof clusters comprise a cluster of one or more of the input medicalimages associated with the disease and one or more clusters of one ormore of the input medical images not associated with the disease. In oneembodiment, the disease is COVID-19 (coronavirus disease 2019).

In one embodiment, the input medical images are clustered by performingunsupervised hierarchical clustering based on a distance between eachpair of images in the input medical images. The distance between eachpair of images in the input medical images is computed by computing aninitial distance between same metrics of the one or more of theplurality of metrics for each respective pair of images and averagingthe initial distances between the same metrics for each respective pairof images.

In one embodiment, the input medical images are clustered by performinga supervised classification using a random forest classifier and alogistic regression classifier.

In one embodiment, the one or more of the plurality of metrics areselected that most discriminate medical images associated with thedisease from medical images not associated with the disease. Theplurality of metrics for the disease represent the distribution,location, and extent of the disease.

In accordance with one or more embodiments, systems and methods forautomatically detecting a disease in medical images are provided. Aninput medical image of lungs of a patient is received. The lungs aresegmented from the input medical image. A probability map forabnormality patterns associated with a disease is generated from theinput medical image. A classification of the input medical image isdetermined based on the segmented lungs and the probability map. Theclassification represents whether the input medical image is associatedwith the disease.

In one embodiment, the disease is COVID-19 and the abnormality patternsassociated with COVID-19 comprise opacities of one or more of groundglass opacities (GGO), consolidation, and crazy-paving pattern.

In one embodiment, the classification of the input medical image is anindication that the input medical image is associated with the diseaseor an indication that the input medical image is not associated with thedisease.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a method of a metrics-based approach for classifyingmedical images, in accordance with one or more embodiments;

FIG. 2A shows images depicting the periphery regions of the lungs, inaccordance with one or more embodiments;

FIG. 2B shows images depicting the rind of the lungs and the core of thelungs, in accordance with one or more embodiments;

FIG. 3 shows a framework for classifying a disease in a medical image,in accordance with one or more embodiments;

FIG. 4 shows a method for classifying a disease in a medical image, inaccordance with one or more embodiments;

FIG. 5 shows a table showing division of a dataset for training,validation, and testing, in accordance with one or more embodiments;

FIG. 6 shows heatmaps of hierarchical clustering generated according tothe metrics-based approach, in accordance with one or more embodiments;

FIG. 7 shows a graph comparing the TPR (true positive rate) against theFPR (false positive rate) for the classifiers utilized for themetrics-based approach and the deep learning-based approach, inaccordance with one or more embodiments;

FIG. 8 shows confusion matrices for classifiers utilized for themetrics-based approach and the deep learning-based approach, inaccordance with one or more embodiments;

FIG. 9 shows an exemplary artificial neural network that may be used toimplement one or more embodiments described herein;

FIG. 10 shows a convolutional neural network that may be used toimplement one or more embodiments described herein; and

FIG. 11 shows a high-level block diagram of a computer that may be usedto implement one or more embodiments described herein.

DETAILED DESCRIPTION

The present invention generally relates to methods and systems forautomatic detection of COVID-19 (coronavirus disease 2019) in chest CT(computed tomography) images. Embodiments of the present invention aredescribed herein to give a visual understanding of such methods andsystems. A digital image is often composed of digital representations ofone or more objects (or shapes). The digital representation of an objectis often described herein in terms of identifying and manipulating theobjects. Such manipulations are virtual manipulations accomplished inthe memory or other circuitry/hardware of a computer system.Accordingly, is to be understood that embodiments of the presentinvention may be performed within a computer system using data storedwithin the computer system.

COVID-19 is an infectious disease that typically presents suchrespiratory symptoms as fever, cough, and difficulty breathing.Typically, patients suspected of, or confirmed as, having COVID-19receive CT imaging of the chest in order to assess the lungs of thepatient. For patients with COVID-19, such CT imaging depicts abnormalitypatterns associated with COVID-19. However, other pulmonary diseases,such as, e.g., other infections (e.g., influenza), malignancy, ILD(interstitial lung disease), and COPD (chronic obstructive pulmonarydisease), similarly manifest in the lungs of the patient, and thus suchCT imaging of patients with other pulmonary diseases may depict similarabnormality patterns.

Embodiments described herein distinguish CT images of abnormalitypatterns associated with COVID-19 from CT images of abnormality patternsassociated with other pulmonary diseases and from CT images of patternsassociated with healthy tissue to provide for automatic detection ofCOVID-19 in CT images. In one embodiment, a metrics-based approach isperformed to automatically detect COVID-19 in CT images, as describedwith respect to, e.g., FIGS. 1 and 4. In another embodiment, a deeplearning-based approach is performed to automatically detect COVID-19 inCT images, as described with respect to, e.g., FIGS. 3-4.Advantageously, the automatic detection of COVID-19 in CT images, inaccordance with embodiments described herein, may be utilized to augmentradiologist diagnostic accuracy and efficiency.

It should be understood that while embodiments described herein aredescribed with respect to detection of COVID-19 in medical images, suchembodiments are not so limited. Embodiments may be applied for thedetection of any disease, such as, e.g., other types of viral pneumonia(e.g., SARS (severe acute respiratory syndrome), MERS (Middle Eastrespiratory syndrome), etc.), bacterial pneumonia, fungal pneumonia,mycoplasma pneumonia, and other types of pneumonia and other types ofdiseases (e.g., ILD, COPD). Further, as used herein, COVID-19 includesmutations of the COVID-19 virus (which may be referred to by differentterms).

FIG. 1 shows a method 100 of a metrics-based approach for classifyingmedical images, in accordance with one or more embodiments. Method 100may be performed by one or more suitable computing devices, such as,e.g., computer 1102 of FIG. 11.

At step 102, input medical images are received. In one embodiment, theinput medical images comprise images of lungs of patients with a diseaseand/or without a disease (i.e., healthy tissue). The disease may includeCOVID-19, pneumonia, ILD, COPD, etc. Accordingly, the input medicalimages may comprise images depicting abnormality patterns associatedwith the disease. For example, where the disease is COVID-19, the inputmedical images may show opacities such as, e.g., GGO (ground glassopacity), consolidation, crazy-paving pattern, atelectasis, interlobularseptal thickening, pleural effusions, bronchiectasis, etc.

In one embodiment, the input medical images are CT input medical images.However, the input medical images may be of any suitable modality, suchas, e.g., MRI (magnetic resonance imaging), US (ultrasound), x-ray, orany other modality or combination of modalities. The input medicalimages may comprise 2D images or 3D volumes, and each input medicalimage may be a single image (or volume) or a plurality of images (e.g.,a time sequence of images). The input medical images may be receiveddirectly from an image acquisition device, such as, e.g., a CT scanner,as the input medical images are acquired, or can be received by loadingpreviously acquired input medical images from a storage or memory of acomputer system or receiving the input medical images from a remotecomputer system.

At step 104, a plurality of metrics for a disease are computed for eachof the input medical images. In one embodiment, the disease is COVID-19,but the disease may be any other disease (e.g., pneumonia, ILD, COPD, orother lung diseases). In one embodiment, the metrics are computed byfirst segmenting the lungs and lobes of the lungs from the input medicalimages. Abnormality patterns associated with the disease are thenidentified, e.g., using a DenseUNet. Based on the segmented lungs andlobes and the identified abnormality patterns, the metrics for thedisease are computed. The metrics represent the severity (e.g., thedistribution, location, and extent) of the disease in the lungs.

In one embodiment, the lungs and lobes are segmented from the inputmedical images by first detecting anatomical landmarks throughout theinput medical images using multi-scale deep reinforcement learning.Regions of interest (ROI) of the input medical images are then extractedbased on the detected landmarks. Specifically, the lung ROI is extractedusing the detected landmark of the carina bifurcation. Other detectedlandmarks may additionally or alternatively be utilized. For example,the sternum tip may be used to extract the lung ROI from the inputmedical images where the carina bifurcation is beyond the image field ofview of the input medical images. The size and the relative location ofthe lung ROI towards the carina bifurcation (or other detected landmark)are specified according to annotated data. Next, the extracted lung ROIimage is resampled to, e.g., a 2 mm isotropic volume and fed into atrained deep image-to-image network (DI2IN) to generate a segmentationmask within the lung ROI. Finally, the segmentation mask is transferredto a unique mask having the same dimension and resolution as the inputmedical image. The unique mask is output as the final lung segmentationmask. The lobes may be similarly segmented. The DI2IN is trained duringa prior offline or training stage. In one embodiment, the DI2IN istrained on a cohort of patients without the prevalence of viralpneumonia and fine-tuned on another cohort with abnormality regionsincluding consolidation, effusions, masses, etc. to improve therobustness of the lung segmentation over the infected area.

In one embodiment, for example where the disease is COVID-19, thirtymetrics for COVID-19 are computed. The thirty metrics are as follows:

-   -   Metrics 1-6: Percent of Opacity (PO) computed as the total        percent volume of the lung parenchyma affected by the disease        for each of the five lobes of the lungs and for the lungs as a        whole.    -   Metrics 7-12: Percent of High Opacity (PHO) computed as the        total percent volume of the lung parenchyma that is severely        affected by the disease for each of the five lobes of the lungs        and for the lungs as a whole. Regions of the lung parenchyma        that are severely affected may be high opacity regions (e.g.,        abnormality pattern regions with a mean HU (Hounsfield units)        greater than −200, corresponding to consolidation and vascular        thickening).    -   Metrics 13-18: Percentage of High Opacity 2 (PHO2) computed as        the total percent volume of the lung parenchyma affected by        denser airspace disease for each of the five lobes of the lungs        and for the lungs as a whole. Regions of the lung parenchyma        affected by denser airspace disease may be high opacity regions        (e.g., abnormality pattern regions with a mean HU between −200        and 50, corresponding to consolidation).    -   Metric 19: Lung Severity Score (LSS) computed as the sum of a        severity score of each of the five lobes of the lungs. In one        embodiment, the severity score for each lobe is based on the PO        for each lobe. For example, a severity score of a lobe may be: 0        if a lobe is not affected by the disease, 1 if the lobe has        1-25% PO, 2 if the lobe has 26-50% PO, 3 if the lobe has 51-75%        PO, and 4 if the lobe has 76-100% PO. The severity score for        computing LSS may be based on any other suitable metric.    -   Metric 20: Lung High Opacity Score (LHOS) computed as the sum of        a severity score of each of the five lobes of the lungs for high        opacity regions only. In one embodiment, the severity score for        each lobe is based on the PHO for each lobe. For example, a        severity score of a lobe may be: 0 if a lobe is not affected by        the disease, 1 if the lobe has 1-25% PHO, 2 if the lobe has        26-50% PHO, 3 if the lobe has 51-75% PHO, and 4 if the lobe has        76-100% PHO. The severity score for computing LHOS may be based        on any other suitable metric.    -   Metric 21: Lung High Opacity Score 2 (LHOS2) computed as the sum        of a severity score for each of the five lobes of the lungs for        high opacity regions excluding vasculature. Vasculature may be        identified based on threshold (e.g., regions with a HU above 50        may be excluded. In one embodiment, the severity score for each        lobe is based on the PHO for each lobe. For example, a severity        score of a lobe may be: 0 if a lobe is not affected by the        disease, 1 if the lobe has 1-25% PHO, 2 if the lobe has 26-50%        PHO, 3 if the lobe has 51-75% PHO, and 4 if the lobe has 76-100%        PHO. The severity score for computing LHOS2 may be based on any        other suitable metric.    -   Metric 22: Bilaterality determined as true if both lungs are        affected by the disease and false if only one or none of the        lungs are affected by the disease.    -   Metric 23: Number of lobes affected by the disease.    -   Metric 24: Number of total lesions in the lungs.    -   Metric 25: Number of peripheral lesions determined as the number        of lesions that are in the periphery of the lungs (which        excludes the apex and mediastinal regions). FIG. 2A shows images        200 depicting the periphery regions of the lungs, in accordance        with one or more embodiments.    -   Metric 26: Number of lesions in the rind of the lungs. Any        abnormality that intersects with the rind is considered a lesion        in the rind. FIG. 2B shows images 210 depicting the rind of the        lungs, in accordance with one or more embodiments.    -   Metric 27: Number of lesions in the core of the lungs. Any        abnormality that does not intersect with the rind is considered        a lesion in the core. Images 210 in FIG. 2B shows the core of        the lungs.    -   Metric 28: Percent of peripheral distribution computed as the        number of peripheral lesions divided by the number of total        lesions.    -   Metric 29: Percent of peripheral lesions computed as the total        percent volume of the lung parenchyma affected by the disease        for peripheral lesions only.    -   Metric 30: Percent of GGO computed as the total percent volume        of the lung parenchyma affected by less dense airspace disease        (i.e., lesions characterized as GGO only). GGO is the        abnormality pattern regions with a mean HU less than −200.

At step 106, the input medical images are clustered into a plurality ofclusters based on one or more of the plurality of metrics to classifythe input medical images. The plurality of clusters comprise a clusterof one or more of the input medical images that are associated with thedisease and one or more clusters of one or more input medical imagesthat are not associated with the disease (i.e., associated with otherdiseases or associated with healthy tissue).

In one embodiment, the one or more of the plurality of metrics areselected, from the plurality of metrics, as the metrics that mostdiscriminate between abnormality patterns associated with the diseaseand patterns not associated with the disease (i.e., abnormality patternsassociated with other diseases or patterns associated with healthytissue). The one or more of the plurality of metrics may be selectedusing mutual information based on an internal validation split.

In one embodiment, the input medical images are clustered based on theone or more of the plurality of the selected metrics using unsupervisedhierarchical cluster analysis to cluster input medical images that havesimilar features. A distance matrix is computed by calculating, for eachpair of the input medical images, an initial distance between samemetrics of the one or more of the plurality of metrics. For example, theinitial distance between the PO metric is calculated for each pair ofinput medical images or the initial distance between the PHO metric iscalculated for each pair of input medical images. The initial distancemay be any suitable distance measure, such as, e.g., the pairwiseEuclidean distance. Average linkage clustering is then used tohierarchically cluster the input medical images using the average of theinitial distances between the same metrics for each pair of inputmedical images.

In one embodiment, the input medical images are clustered based on theone or more of the plurality of metrics using supervised classification.Two metrics-based classifiers are trained. First, a random forestclassifier is trained using the one or more of the plurality of metrics.Subsequently, a logistic regression classifier is trained after afeature transformation based on gradient boosted trees on all of theplurality of metrics. The random forest classifier and the logisticregression classifier are trained during a prior offline or trainingstage. Once trained, the random forest classifier and the logisticregression classifier are applied at step 106 during an online orinference stage. For instance, the plurality of selected metrics arecomputed and the random forest classifier and the logistic regressionclassifier are applied to provide a class score, which is used toclassify the images. In one embodiment, the gradient boosted trees weretrained using 2000 estimators with a max depth of 3 and 3 features foreach split. A boosting fraction of 0.8 was used for fitting theindividual trees. The LR classifier was trained with L2 regularization(C=0.1). Class weights were adjusted to class frequencies to addressclass imbalance between disease cases and non-disease cases.

At step 108, the classification of the input medical images is output.For example, the classification of the input medical images can beoutput by displaying the classification of the input medical images on adisplay device of a computer system, storing the classification of theinput medical images on a memory or storage of a computer system, or bytransmitting the classification of the input medical images to a remotecomputer system.

In one embodiment, the classification of the input medical images may beoutput as a heatmap. Exemplary heatmaps are shown in FIG. 6, which isdescribed in more detail below.

FIG. 3 shows a framework 300 for classifying a disease in a medicalimage, in accordance with one or more embodiments. FIG. 4 shows a method400 for classifying a disease in a medical image, in accordance with oneor more embodiments. FIGS. 3 and 4 will be described together. The stepsof FIG. 4 may be performed by one or more suitable computing devices,such as, e.g., computer 1102 of FIG. 11.

At step 402, an input medical image of lungs of a patient is received.In one embodiment, the input medical image is a CT medical image.However, the input medical image may be of any suitable modality, suchas, e.g., MRI, US, x-ray, or any other modality or combination ofmodalities. The input medical image may comprise a 2D image or 3Dvolume, and may be a single image or a plurality of images (e.g., a timesequence of images). The input medical image may be received directlyfrom an image acquisition device, such as, e.g., a CT scanner, as theinput medical image is acquired, or can be received by loading apreviously acquired input medical image from a storage or memory of acomputer system or receiving an input medical image from a remotecomputer system.

At step 404, the lungs are segmented from the input medical image. Inone example, the lungs are segmented at preprocessing step 302 of FIG.3. The lungs may be segmented from the input medical image as describedabove with respect to step 104 of FIG. 1.

At step 406, a probability map for abnormality patterns associated witha disease is generated from the input medical image. In one example, theprobability map is generated at preprocessing step 302 of FIG. 3. In oneembodiment, the disease is COVID-19 and the abnormality regionsassociated with COVID-19 include opacities such as, e.g., GGO,consolidation, crazy-paving pattern, atelectasis, interlobular septalthickening, pleural effusions, bronchiectasis, etc. However, the diseasemay be any other disease (e.g., pneumonia, ILD, COPD, or other lungdiseases).

The probability map for abnormality patterns associated with the diseasemay be generated using a machine learning based opacity classifier, suchas, e.g., a DenseUNet. However, any other suitable machine learningbased network may be applied for generating a probability map. TheDenseUNet with anisotropic kernels is trained to transfer the inputmedical image to a probability map of the same size. All voxels in thelungs that fully or partially comprise GGO, consolidations, orcrazy-paving patterns (or any other type of abnormality associated withthe disease) are defined as positive voxels. The remainder of the imagearea within the lungs and the entire area outside the lungs are definedas negative voxels. The DenseUNet is trained in an end-to-end system. Aninitial probability mask generated by the DenseUNet is filtered usingthe segmented lungs so that only the abnormality regions present withinthe lungs are identified. The filtered probability mask is output as afinal probability map for abnormality patterns associated with thedisease. The final probability map may be overlaid on the input medicalimage. In one embodiment, the probability map may be converted to abinary segmentation mask based on a threshold (e.g., 0.5).

At step 408, a classification of the input medical image is determinedbased on the segmented lung and the probability map. The classificationrepresents whether the input medical image is associated with thedisease. In one example, the classification is a score between 0 and 1.The classification may be a binary classification (e.g., yes or no) thatthe input medical image is associated with the disease or that the inputmedical image is not associated with the disease based on the scoreusing a threshold.

In one embodiment, the classification of the input medical image isdetermined using a machine learning based classifier. For example, theclassifier may be 3D deep learning classifier 304 in FIG. 3. Theclassifier receives as input the input medical image masked by thesegmented lung and the probability map. In one embodiment, theclassifier uses anisotropic 3D kernels to balance resolution and speed,and comprises deep dense blocks that gradually aggregate features downto a binary output. The classifier may be trained during a prior offlineor training stage in an end-to-end manner as a classification systemusing binary cross entropy and uses probabilistic sampling of thetraining data to adjust for the imbalance in the training datasetlabels. Once trained, the classifier is applied at step 408 during anonline or inference stage.

At step 410, the classification of the input medical image is output. Inone example, the classification of the input medical image is output asoutput 306 of FIG. 3 representing a yes or no indication that either theinput medical image is associated with the disease or that the inputmedical image is not associated with the disease. The classification ofthe input medical image can be output by displaying the classificationof the input medical image on a display device of a computer system,storing the classification of the input medical image on a memory orstorage of a computer system, or by transmitting the classification ofthe input medical image to a remote computer system.

The metrics-based approach (as described with respect to, e.g., FIG. 1)and the deep learning-based approached (as described with respect to,e.g., FIG. 4), in accordance with embodiments described herein, wereexperimentally validated for detecting COVID-19 using a dataset of 2,096CT images, which included 1150 CT images of patients with COVID-19 and946 CT images of patients without COVID-19. The 946 CT images ofpatients without COVID-19 included 159 CT images of patients withpneumonia, 177 CT images of patients with ILD, and 610 CT images withoutany lung disease. The CT images were acquired from 16 different clinicalcenters in North America and Europe. The CT images of patients withCOVID-19 acquired from North America were confirmed via RT-PCR testing,while the CT images of patients with COVID-19 acquired from Europe 19were confirmed by either RT-PCR testing or diagnosed by a clinicianbased on clinical symptoms, epidemiological exposure, and radiologicalassessment. The pneumonia cohort comprised cases of patients withnon-COVID-19 viral pneumonias, organizing pneumonia, or aspirationpneumonia. The ILD cohort comprised patients with various types of ILDexhibiting GGO, reticulation, honeycombing, and consolidation todifferent degrees. The dataset was divided into training, validation,and testing datasets. Model training and selection was performed basedon the training and validation sets. FIG. 5 shows a table 500 showingdivision of the dataset for training, validation, and testing.

The metrics-based approach was implemented using a deep image-to-imagenetwork trained on a large cohort of healthy and abnormal cases forsegmentation of the lungs and lobes of the lungs. A DenseUNet was usedto identify abnormality patterns associated with COVID-19. Thirtymetrics (as described above with respect to step 104 of FIG. 1) werecomputed representing the severity of COVID-19. Seven metrics that weremost discriminative between COVID-19 and non-COVID-19 patterns wereselected by comparing mutual information between the metrics and theclass in the training dataset of 999 COVID-19 cases and 801 controlscases (pneumonia, ILD, and healthy). One COVID-19 case was excluded fromtraining due to field of view issues, one pneumonia control case wasexcluded since the z-axis resolution was less than 10 mm, and anotherpneumonia control case was excluded due to incorrect DICOM (digitalimaging and communications in medicine) parameters and artifact issues.

The selected metrics were percent of GGO, PHO2 (corresponding toconsolidation), PO (corresponding to consolidation and GGO), percent ofopacities in the periphery, percent of opacities in the rind, percent ofopacities in the right lower lobe, and percent of opacities in the leftlower lobe. The selected metrics correspond to typical COVID-19characteristics (i.e., multifocal GGO and consolidation with basilar andperipheral distribution of the disease) reported in clinical literature.

FIG. 6 shows heatmaps of hierarchical clustering generated according tothe metrics-based approach, in accordance with one or more embodiments.Heatmap 602 shows hierarchical clustering on the training dataset andheatmap 604 shows hierarchical clustering on the test dataset. Theground truth diagnosis cohort membership (COVID-19, pneumonia, ILD, andhealthy) is shown by shading (or color). The metric values arestandardized and rescaled to a value between 0 and 1. The probability ofbelonging to the COVID-19 class increases towards the bottom of eachheatmap 602 and 604, which corresponds to higher values of the metrics(i.e., more opacities (both GGO and consolidation) and more peripheraland basilar distribution). In heatmap 602, the clustering is performedon the entire training set of 1800 patients. The middle of heatmap 602shows an ambiguous region, where there is an overlap of features fromdifferent disease cohorts. Heatmap 604 shows the same clustering in thetest dataset for each of the disease cohorts. While there is a clusterof COVID-19 subjects that have characteristic features, there are alsomany which do not show all characteristics. Moreover, some cases ofpneumonia and ILD overlap with typical features of COVID-19.

The deep learning-based approach was implemented using a deeplearning-based 3D neural network model trained to separate the positiveclass (COVID-19 class) from the negative class (non-COVID-19 class). Atwo-channel 3D tensor, with a first channel comprising the CT imagemasked by the lung segmentation and a second channel comprising aprobability map of abnormality patterns associated with COVID-19. The 3Dnetwork used anisotropic 3D kernels to balance resolution and speed, andwas formed of deep dense blocks that gradually aggregate features downto a binary output. The network was trained in an end-to-end manner as aclassification system using binary cross entropy and probabilisticsampling of the training data to adjust for the imbalance in thetraining dataset labels. A separate validation dataset was used forfinal model selection before performance was measured on the testingset. The input 3D tensor size was fixed (2×128×384×384) corresponding tothe lung segmentation from the CT image rescaled to a 3×1×1 mmresolution. The first two blocks were anisotropic comprising convolution(kernels 1×3×3)-batch normalization-LeakyReLU (leaky rectified linearunit) and max-pooling (kernels 1×2×2, stride 1×2×2). The subsequent fiveblocks were isotropic with convolution (kernels 3×3×3)-batchnormalization-LeakyReLU and max-pooling (kernels 2×2×2, stride 2×2×2)followed by a final linear classifier with the input 144-dimensional.

FIG. 7 shows a graph 700 comparing the TPR (true positive rate) againstthe FPR (false positive rate) for the classifiers utilized for themetrics-based approach and the deep learning-based approach, inaccordance with one or more embodiments. The dashed diagonal line ingraph 700 corresponds to random chance. The random forest classifier,denoted M1, was trained for the metrics-based approach using the sevenselected metrics. As shown in graph 700, the performance of the randomforest classifier on the test dataset had an AUC (area under curve) of0.80. The red circles denote the optimal operating point, which yieldeda sensitivity of 0.74 and a specificity of 0.73 for the random forestclassifier. The performance of the random forest classifier was improvedby training a logistic regression classifier, denoted M2, on all thirtymetrics. The metrics were first transformed to a higher-dimensionalspace using feature embedding with gradient boosted trees. The logisticregression classifier produces an AUC of 0.85 with a sensitivity of 0.81and a specificity of 0.77. While the performance of the logisticregression classifier improved over the random forest classifier, someof the interpretability was lost since the features were transformed toa higher dimension. The deep learning based classifier, denoted M3, hadthe best performance with an AUC of 0.90, improving the sensitivity andspecificity of the system to 0.86 and 0.81 respectively. The improvementis mostly due to the reduction of the false positives from the ILD andnon-COVID-19 pneumonia categories. The optimal operating point, circledin graph 700, for all models was chosen as the point on the ROC(receiver operating characteristic) curve with the shortest distancefrom the top left corner of graph 700. The corresponding confusionmatrices for all three classifiers is shown in table 800 of FIG. 8.

The unsupervised clustering on the selected metrics showed that whilethere are dominant characteristics that can be observed in COVID-19,such as the presence of GGO as well as peripheral and basaldistribution, these characteristics are not observed in all cases ofCOVID-19. On the other hand, some subjects with ILD and pneumonia canexhibit similar characteristics. It was found that the performance ofthe unsupervised clustering approach can be improved by mapping themetrics into a higher dimensional space prior to training, as shown bythe logistic regression classifier in FIG. 7. The best classificationaccuracy was achieved by the deep learning based approach, which may berepresented as a high-dimensional, non-linear model.

The deep learning approach achieved a reduced false positive and falsenegative rate relative to the metrics-based classifier, suggesting thatthere might be other latent radiological representations of COVID-19that distinguish it from interstitial lung diseases or other types ofpneumonia. The proposed deep learning approach was trained and tested ona dataset of 2096 CT images with 1150 COVID-19 patients and 946 imagescoming from other categories. The proposed deep learning approach wascompared to conventional methods and it was found that the proposed deeplearning approach achieved a higher AUC as well as sensitivity.

The experimental validation was performed using a diverse dataset of CTimages, which were acquired from a variety of manufacturers,institutions, and regions, ensuring that the results are robust andlikely generalizable to different environments. Included in the COVID-19negative class were not only healthy subjects, but also various types oflung pathology (e.g., ILD and pneumonia).

Embodiments described herein provide clinical value in several aspects.Embodiments described herein may be used for rapid triage of positivecases, particularly in resource constrained environments whereradiologic expertise may not be immediately available and RT-PCR resultsmay take up to several hours. Embodiments described herein may helpradiologists to prioritize interpreting CT images in patients withCOVID-19 by screening out lower probability cases. In addition torapidity and efficiency concerns, the output of the deep learningapproach is easily reproducible and replicable, mitigating inter-readervariability in manually read radiology studies. While RT-PCR is thestandard for confirmatory diagnosis of COVID-19, machine learningmethods applied to quantitative CT can be performed for diagnosis ofCOVID-19 with high diagnostic accuracy, increasing the value of imagingin diagnosis and management of COVID-19.

Further, embodiments described herein may be integrated in surveillanceof patients for COVID-19, even in unsuspected patients. For example, allchest CT images for pulmonary and non-pulmonary pathology (e.g.,coronary artery exams, chest trauma evaluation) may be automaticallyassessed for evidence of COVID-19 lung disease, as well as fornon-COVID-19 pneumonia. Referring clinicians may be alerted for COVID-19positive determinations, allowing more rapid institution of isolationprotocols. Finally, embodiments described herein may be appliedretrospectively to large numbers of chest CT images from institutionalPACS (picture archiving and communication system) worldwide to uncoverthe origin and trace the diffuse of SARS-CoV-2 in communities prior tothe implementation of widespread testing efforts.

Embodiments described herein may be deployed and validated in a clinicalsetting to evaluate the clinical utility and diagnostic accuracy onprospective data, as well as to determine the correlation of the variousmetrics described herein with the clinical severity of COVID-19 anddisease progression over time. COVID-19 severity can be furtherquantified by using features from contrast CT angiography, such asdetection and measurement of acute pulmonary embolism, which wasreported to be associated with severe COVID-19 infections. In addition,classifiers described herein may be improved by incorporating otherclinical data in the training, such as pulse oximetry, cell counts,liver enzymes, etc., in addition to imaging features.

Embodiments described herein are described with respect to the claimedsystems as well as with respect to the claimed methods. Features,advantages or alternative embodiments herein can be assigned to theother claimed objects and vice versa. In other words, claims for thesystems can be improved with features described or claimed in thecontext of the methods. In this case, the functional features of themethod are embodied by objective units of the providing system.

Furthermore, embodiments described herein are described with respect tomethods and systems for automatic detection of COVID-19 in chest CTimages using a trained machine learning based network, as well as withrespect to methods and systems for training a machine learning basednetwork for automatic detection of COVID-19 in chest CT images.Features, advantages or alternative embodiments herein can be assignedto the other claimed objects and vice versa. In other words, claims formethods and systems for training a machine learning based network can beimproved with features described or claimed in context of the methodsand systems for utilizing a trained machine learning based network, andvice versa.

In particular, the trained machine learning based network of the methodsand systems for automatic detection of COVID-19 in chest CT images canbe adapted by the methods and systems for training the machine learningbased network for automatic detection of COVID-19 in chest CT images.Furthermore, the input data of the trained machine learning basednetwork can comprise advantageous features and embodiments of thetraining input data, and vice versa. Furthermore, the output data of thetrained machine learning based network can comprise advantageousfeatures and embodiments of the output training data, and vice versa.

In general, a trained machine learning based network mimics cognitivefunctions that humans associate with other human minds. In particular,by training based on training data, the trained machine learning basednetwork is able to adapt to new circumstances and to detect andextrapolate patterns.

In general, parameters of a machine learning based network can beadapted by means of training. In particular, supervised training,semi-supervised training, unsupervised training, reinforcement learningand/or active learning can be used. Furthermore, representation learning(an alternative term is “feature learning”) can be used. In particular,the parameters of the trained machine learning based network can beadapted iteratively by several steps of training.

In particular, a trained machine learning based network can comprise aneural network, a support vector machine, a decision tree, and/or aBayesian network, and/or the trained machine learning based network canbe based on k-means clustering, Q-learning, genetic algorithms, and/orassociation rules. In particular, a neural network can be a deep neuralnetwork, a convolutional neural network, or a convolutional deep neuralnetwork. Furthermore, a neural network can be an adversarial network, adeep adversarial network and/or a generative adversarial network.

FIG. 9 shows an embodiment of an artificial neural network 900, inaccordance with one or more embodiments. Alternative terms for“artificial neural network” are “neural network”, “artificial neuralnet” or “neural net”. Machine learning networks described herein, suchas, e.g., random forest classifier and the logistic regressionclassifier utilized at step 106 of FIG. 1 or the classifier utilized atstep 408 of FIG. 4, may be implemented using artificial neural network900.

The artificial neural network 900 comprises nodes 902-922 and edges 932,934, . . . , 936, wherein each edge 932, 934, . . . , 936 is a directedconnection from a first node 902-922 to a second node 902-922. Ingeneral, the first node 902-922 and the second node 902-922 aredifferent nodes 902-922, it is also possible that the first node 902-922and the second node 902-922 are identical. For example, in FIG. 9, theedge 932 is a directed connection from the node 902 to the node 906, andthe edge 934 is a directed connection from the node 904 to the node 906.An edge 932, 934, . . . , 936 from a first node 902-922 to a second node902-922 is also denoted as “ingoing edge” for the second node 902-922and as “outgoing edge” for the first node 902-922.

In this embodiment, the nodes 902-922 of the artificial neural network900 can be arranged in layers 924-930, wherein the layers can comprisean intrinsic order introduced by the edges 932, 934, . . . , 936 betweenthe nodes 902-922. In particular, edges 932, 934, . . . , 936 can existonly between neighboring layers of nodes. In the embodiment shown inFIG. 9, there is an input layer 924 comprising only nodes 902 and 904without an incoming edge, an output layer 930 comprising only node 922without outgoing edges, and hidden layers 926, 928 in-between the inputlayer 924 and the output layer 930. In general, the number of hiddenlayers 926, 928 can be chosen arbitrarily. The number of nodes 902 and904 within the input layer 924 usually relates to the number of inputvalues of the neural network 900, and the number of nodes 922 within theoutput layer 930 usually relates to the number of output values of theneural network 900.

In particular, a (real) number can be assigned as a value to every node902-922 of the neural network 900. Here, x^((n)) _(i) denotes the valueof the i-th node 902-922 of the n-th layer 924-930. The values of thenodes 902-922 of the input layer 924 are equivalent to the input valuesof the neural network 900, the value of the node 922 of the output layer930 is equivalent to the output value of the neural network 900.Furthermore, each edge 932, 934, . . . , 936 can comprise a weight beinga real number, in particular, the weight is a real number within theinterval [−1, 1] or within the interval [0, 1]. Here, w^((m,n)) _(i,j)denotes the weight of the edge between the i-th node 902-922 of the m-thlayer 924-930 and the j-th node 902-922 of the n-th layer 924-930.Furthermore, the abbreviation w^((n)) _(i,j) is defined for the weightw^((n,n+1)) _(i,j).

In particular, to calculate the output values of the neural network 900,the input values are propagated through the neural network. Inparticular, the values of the nodes 902-922 of the (n+1)-th layer924-930 can be calculated based on the values of the nodes 902-922 ofthe n-th layer 924-930 by

x _(j) ^((n+1)) =f(Σ_(i) x _(i) ^((n)) ·w _(i,j) ^((n))).

Herein, the function f is a transfer function (another term is“activation function”). Known transfer functions are step functions,sigmoid function (e.g. the logistic function, the generalized logisticfunction, the hyperbolic tangent, the Arctangent function, the errorfunction, the smoothstep function) or rectifier functions The transferfunction is mainly used for normalization purposes.

In particular, the values are propagated layer-wise through the neuralnetwork, wherein values of the input layer 924 are given by the input ofthe neural network 900, wherein values of the first hidden layer 926 canbe calculated based on the values of the input layer 924 of the neuralnetwork, wherein values of the second hidden layer 928 can be calculatedbased in the values of the first hidden layer 926, etc.

In order to set the values w^((m,n)) _(i,j) for the edges, the neuralnetwork 900 has to be trained using training data. In particular,training data comprises training input data and training output data(denoted as t_(i)). For a training step, the neural network 900 isapplied to the training input data to generate calculated output data.In particular, the training data and the calculated output data comprisea number of values, said number being equal with the number of nodes ofthe output layer.

In particular, a comparison between the calculated output data and thetraining data is used to recursively adapt the weights within the neuralnetwork 900 (backpropagation algorithm). In particular, the weights arechanged according to

w′ _(i,j) ^((n)) =w _(i,j) ^((n))−γ·δ_(j) ^((n)) ·x _(i) ^((n))

wherein γ is a learning rate, and the numbers δ^((n)) _(j) can berecursively calculated as

δ_(j) ^((n))=(Σ_(k)δ_(k) ^((n+1)) ·w _(j,k) ^((n+1)))·f′(Σ_(i) x _(i)^((n)) ·w _(i,j) ^((n)))

based on δ^((n+1)) _(j), if the (n+1)-th layer is not the output layer,and

δ_(j) ^((n))=(x _(k) ^((n+1)) −t _(j) ^((n+1)))·f′(Σ_(i) x _(i) ^((n))·w _(i,j) ^((n)))

if the (n+1)-th layer is the output layer 930, wherein f′ is the firstderivative of the activation function, and y^((n+1)) _(j) is thecomparison training value for the j-th node of the output layer 930.

FIG. 10 shows a convolutional neural network 1000, in accordance withone or more embodiments. Machine learning networks described herein,such as, e.g., random forest classifier and the logistic regressionclassifier utilized at step 106 of FIG. 1 or the classifier utilized atstep 408 of FIG. 4, may be implemented using convolutional neuralnetwork 1000.

In the embodiment shown in FIG. 10, the convolutional neural networkcomprises 1000 an input layer 1002, a convolutional layer 1004, apooling layer 1006, a fully connected layer 1008, and an output layer1010. Alternatively, the convolutional neural network 1000 can compriseseveral convolutional layers 1004, several pooling layers 1006, andseveral fully connected layers 1008, as well as other types of layers.The order of the layers can be chosen arbitrarily, usually fullyconnected layers 1008 are used as the last layers before the outputlayer 1010.

In particular, within a convolutional neural network 1000, the nodes1012-1020 of one layer 1002-1010 can be considered to be arranged as ad-dimensional matrix or as a d-dimensional image. In particular, in thetwo-dimensional case the value of the node 1012-1020 indexed with i andj in the n-th layer 1002-1010 can be denoted as x^((n)) _([i,j]).However, the arrangement of the nodes 1012-1020 of one layer 1002-1010does not have an effect on the calculations executed within theconvolutional neural network 1000 as such, since these are given solelyby the structure and the weights of the edges.

In particular, a convolutional layer 1004 is characterized by thestructure and the weights of the incoming edges forming a convolutionoperation based on a certain number of kernels. In particular, thestructure and the weights of the incoming edges are chosen such that thevalues x^((n)) _(k) of the nodes 1014 of the convolutional layer 1004are calculated as a convolution x^((n)) _(k)=K_(k)*x^((n−1)) based onthe values x^((n−1)) of the nodes 1012 of the preceding layer 1002,where the convolution * is defined in the two-dimensional case as

x _(k) ^((n))[i,j]=(K _(k) *x ^((n−1)))[i,j]=Σ_(i′)Σ_(j′) K _(k[)i′,j′]·x ^((n−1))[i−i′,j−j′].

Here the k-th kernel K_(k) is a d-dimensional matrix (in this embodimenta two-dimensional matrix), which is usually small compared to the numberof nodes 1012-1018 (e.g. a 3×3 matrix, or a 5×5 matrix). In particular,this implies that the weights of the incoming edges are not independent,but chosen such that they produce said convolution equation. Inparticular, for a kernel being a 3×3 matrix, there are only 9independent weights (each entry of the kernel matrix corresponding toone independent weight), irrespectively of the number of nodes 1012-420in the respective layer 1002-1010. In particular, for a convolutionallayer 1004, the number of nodes 1014 in the convolutional layer isequivalent to the number of nodes 1012 in the preceding layer 1002multiplied with the number of kernels.

If the nodes 1012 of the preceding layer 1002 are arranged as ad-dimensional matrix, using a plurality of kernels can be interpreted asadding a further dimension (denoted as “depth” dimension), so that thenodes 1014 of the convolutional layer 1014 are arranged as a(d+1)-dimensional matrix. If the nodes 1012 of the preceding layer 1002are already arranged as a (d+1)-dimensional matrix comprising a depthdimension, using a plurality of kernels can be interpreted as expandingalong the depth dimension, so that the nodes 1014 of the convolutionallayer 1004 are arranged also as a (d+1)-dimensional matrix, wherein thesize of the (d+1)-dimensional matrix with respect to the depth dimensionis by a factor of the number of kernels larger than in the precedinglayer 1002.

The advantage of using convolutional layers 1004 is that spatially localcorrelation of the input data can exploited by enforcing a localconnectivity pattern between nodes of adjacent layers, in particular byeach node being connected to only a small region of the nodes of thepreceding layer.

In embodiment shown in FIG. 10, the input layer 1002 comprises 36 nodes1012, arranged as a two-dimensional 6×6 matrix. The convolutional layer1004 comprises 72 nodes 1014, arranged as two two-dimensional 6×6matrices, each of the two matrices being the result of a convolution ofthe values of the input layer with a kernel. Equivalently, the nodes1014 of the convolutional layer 1004 can be interpreted as arranges as athree-dimensional 6×6×2 matrix, wherein the last dimension is the depthdimension.

A pooling layer 1006 can be characterized by the structure and theweights of the incoming edges and the activation function of its nodes1016 forming a pooling operation based on a non-linear pooling functionf. For example, in the two dimensional case the values x^((n)) of thenodes 1016 of the pooling layer 1006 can be calculated based on thevalues x^((n−1)) of the nodes 1014 of the preceding layer 1004 as

x ^((n))[i,j]=f(x ^((n−1))[id ₁ ,jd ₂], . . . ,x ^((n−1))[id ₁ +d ₁−1,jd₂ +d ₂−1])

In other words, by using a pooling layer 1006, the number of nodes 1014,1016 can be reduced, by replacing a number d1·d2 of neighboring nodes1014 in the preceding layer 1004 with a single node 1016 beingcalculated as a function of the values of said number of neighboringnodes in the pooling layer. In particular, the pooling function f can bethe max-function, the average or the L2-Norm. In particular, for apooling layer 1006 the weights of the incoming edges are fixed and arenot modified by training.

The advantage of using a pooling layer 1006 is that the number of nodes1014, 1016 and the number of parameters is reduced. This leads to theamount of computation in the network being reduced and to a control ofoverfitting.

In the embodiment shown in FIG. 10, the pooling layer 1006 is amax-pooling, replacing four neighboring nodes with only one node, thevalue being the maximum of the values of the four neighboring nodes. Themax-pooling is applied to each d-dimensional matrix of the previouslayer; in this embodiment, the max-pooling is applied to each of the twotwo-dimensional matrices, reducing the number of nodes from 72 to 18.

A fully-connected layer 1008 can be characterized by the fact that amajority, in particular, all edges between nodes 1016 of the previouslayer 1006 and the nodes 1018 of the fully-connected layer 1008 arepresent, and wherein the weight of each of the edges can be adjustedindividually.

In this embodiment, the nodes 1016 of the preceding layer 1006 of thefully-connected layer 1008 are displayed both as two-dimensionalmatrices, and additionally as non-related nodes (indicated as a line ofnodes, wherein the number of nodes was reduced for a betterpresentability). In this embodiment, the number of nodes 1018 in thefully connected layer 1008 is equal to the number of nodes 1016 in thepreceding layer 1006. Alternatively, the number of nodes 1016, 1018 candiffer.

Furthermore, in this embodiment, the values of the nodes 1020 of theoutput layer 1010 are determined by applying the Softmax function ontothe values of the nodes 1018 of the preceding layer 1008. By applyingthe Softmax function, the sum the values of all nodes 1020 of the outputlayer 1010 is 1, and all values of all nodes 1020 of the output layerare real numbers between 0 and 1.

A convolutional neural network 1000 can also comprise a ReLU (rectifiedlinear units) layer. In particular, the number of nodes and thestructure of the nodes contained in a ReLU layer is equivalent to thenumber of nodes and the structure of the nodes contained in thepreceding layer. In particular, the value of each node in the ReLU layeris calculated by applying a rectifying function to the value of thecorresponding node of the preceding layer. Examples for rectifyingfunctions are f(x)=max(0,x), the tangent hyperbolics function or thesigmoid function.

In particular, convolutional neural networks 1000 can be trained basedon the backpropagation algorithm. For preventing overfitting, methods ofregularization can be used, e.g. dropout of nodes 1012-1020, stochasticpooling, use of artificial data, weight decay based on the L1 or the L2norm, or max norm constraints.

In accordance with one embodiment, the neural network used forclassification uses anisotropic 3D kernels to balance resolution andspeed and consists of deep dense blocks that gradually aggregatefeatures down to a binary output. The network was trained end-to-end asa classification system using binary cross entropy and usesprobabilistic sampling of the training data to adjust for the imbalancein the training dataset labels. A separate validation dataset was usedfor final model selection before the performance was measured on thetesting set. The input 3D tensor size is fixed (2×128×384×384)corresponding to the lung segmentation from the CT data rescaled to a3×1×1 mm resolution. The first two blocks are anisotropic and consist ofconvolution (kernels 1×3×3)-batch normalization-LeakyReLU andMax-pooling (kernels 1×2×2, stride 1×2×2). The subsequent five blocksare isotropic with convolution (kernels 3×3×3)-batchnormalization-LeakyReLU and Max-pooling (kernels 2×2×2, stride 2×2×2)followed by a final linear classifier with the input 144-dimensional.

Systems, apparatuses, and methods described herein may be implementedusing digital circuitry, or using one or more computers using well-knowncomputer processors, memory units, storage devices, computer software,and other components. Typically, a computer includes a processor forexecuting instructions and one or more memories for storing instructionsand data. A computer may also include, or be coupled to, one or moremass storage devices, such as one or more magnetic disks, internal harddisks and removable disks, magneto-optical disks, optical disks, etc.

Systems, apparatus, and methods described herein may be implementedusing computers operating in a client-server relationship. Typically, insuch a system, the client computers are located remotely from the servercomputer and interact via a network. The client-server relationship maybe defined and controlled by computer programs running on the respectiveclient and server computers.

Systems, apparatus, and methods described herein may be implementedwithin a network-based cloud computing system. In such a network-basedcloud computing system, a server or another processor that is connectedto a network communicates with one or more client computers via anetwork. A client computer may communicate with the server via a networkbrowser application residing and operating on the client computer, forexample. A client computer may store data on the server and access thedata via the network. A client computer may transmit requests for data,or requests for online services, to the server via the network. Theserver may perform requested services and provide data to the clientcomputer(s). The server may also transmit data adapted to cause a clientcomputer to perform a specified function, e.g., to perform acalculation, to display specified data on a screen, etc. For example,the server may transmit a request adapted to cause a client computer toperform one or more of the steps or functions of the methods andworkflows described herein, including one or more of the steps orfunctions of FIGS. 1 and 4. Certain steps or functions of the methodsand workflows described herein, including one or more of the steps orfunctions of FIGS. 1 and 4, may be performed by a server or by anotherprocessor in a network-based cloud-computing system. Certain steps orfunctions of the methods and workflows described herein, including oneor more of the steps of FIGS. 1 and 4, may be performed by a clientcomputer in a network-based cloud computing system. The steps orfunctions of the methods and workflows described herein, including oneor more of the steps of FIGS. 1 and 4, may be performed by a serverand/or by a client computer in a network-based cloud computing system,in any combination.

Systems, apparatus, and methods described herein may be implementedusing a computer program product tangibly embodied in an informationcarrier, e.g., in a non-transitory machine-readable storage device, forexecution by a programmable processor; and the method and workflow stepsdescribed herein, including one or more of the steps or functions ofFIGS. 1 and 4, may be implemented using one or more computer programsthat are executable by such a processor. A computer program is a set ofcomputer program instructions that can be used, directly or indirectly,in a computer to perform a certain activity or bring about a certainresult. A computer program can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment.

A high-level block diagram of an example computer 1102 that may be usedto implement systems, apparatus, and methods described herein isdepicted in FIG. 11. Computer 1102 includes a processor 1104 operativelycoupled to a data storage device 1112 and a memory 1110. Processor 1104controls the overall operation of computer 1102 by executing computerprogram instructions that define such operations. The computer programinstructions may be stored in data storage device 1112, or othercomputer readable medium, and loaded into memory 1110 when execution ofthe computer program instructions is desired. Thus, the method andworkflow steps or functions of FIGS. 1 and 4 can be defined by thecomputer program instructions stored in memory 1110 and/or data storagedevice 1112 and controlled by processor 1104 executing the computerprogram instructions. For example, the computer program instructions canbe implemented as computer executable code programmed by one skilled inthe art to perform the method and workflow steps or functions of FIGS. 1and 4. Accordingly, by executing the computer program instructions, theprocessor 1104 executes the method and workflow steps or functions ofFIGS. 1 and 4. Computer 1102 may also include one or more networkinterfaces 1106 for communicating with other devices via a network.Computer 1102 may also include one or more input/output devices 1108that enable user interaction with computer 1102 (e.g., display,keyboard, mouse, speakers, buttons, etc.).

Processor 1104 may include both general and special purposemicroprocessors, and may be the sole processor or one of multipleprocessors of computer 1102. Processor 1104 may include one or morecentral processing units (CPUs), for example. Processor 1104, datastorage device 1112, and/or memory 1110 may include, be supplemented by,or incorporated in, one or more application-specific integrated circuits(ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device 1112 and memory 1110 each include a tangiblenon-transitory computer readable storage medium. Data storage device1112, and memory 1110, may each include high-speed random access memory,such as dynamic random access memory (DRAM), static random access memory(SRAM), double data rate synchronous dynamic random access memory (DDRRAM), or other random access solid state memory devices, and may includenon-volatile memory, such as one or more magnetic disk storage devicessuch as internal hard disks and removable disks, magneto-optical diskstorage devices, optical disk storage devices, flash memory devices,semiconductor memory devices, such as erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), compact disc read-only memory (CD-ROM), digital versatile discread-only memory (DVD-ROM) disks, or other non-volatile solid statestorage devices.

Input/output devices 1108 may include peripherals, such as a printer,scanner, display screen, etc. For example, input/output devices 1108 mayinclude a display device such as a cathode ray tube (CRT) or liquidcrystal display (LCD) monitor for displaying information to the user, akeyboard, and a pointing device such as a mouse or a trackball by whichthe user can provide input to computer 1102.

An image acquisition device 1114 can be connected to the computer 1102to input image data (e.g., medical images) to the computer 1102. It ispossible to implement the image acquisition device 1114 and the computer1102 as one device. It is also possible that the image acquisitiondevice 1114 and the computer 1102 communicate wirelessly through anetwork. In a possible embodiment, the computer 1102 can be locatedremotely with respect to the image acquisition device 1114.

Any or all of the systems and apparatus discussed herein, including thesystems and apparatuses used to implement the random forest classifierand the logistic regression classifier utilized at step 106 of FIG. 1 orthe classifier utilized at step 408 of FIG. 4, may be implemented usingone or more computers such as computer 1102.

One skilled in the art will recognize that an implementation of anactual computer or computer system may have other structures and maycontain other components as well, and that FIG. 11 is a high levelrepresentation of some of the components of such a computer forillustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A computer implemented method, comprising: receiving input medicalimages; computing a plurality of metrics for a disease for each of theinput medical images; and clustering the input medical images into aplurality of clusters based on one or more of the plurality of metricsto classify the input medical images, the plurality of clusterscomprising: a cluster of one or more of the input medical imagesassociated with the disease, and one or more clusters of one or more ofthe input medical images not associated with the disease.
 2. Thecomputer implemented method of claim 1, wherein clustering the inputmedical images into a plurality of clusters based on one or more of theplurality of metrics to classify the input medical images comprises:performing unsupervised hierarchical clustering based on a distancebetween each pair of images in the input medical images.
 3. The computerimplemented method of claim 2, further comprising computing the distancebetween each pair of images in the input medical images by: computing aninitial distance between same metrics of the one or more of theplurality of metrics for each respective pair of images; and averagingthe initial distances between the same metrics for each respective pairof images.
 4. The computer implemented method of claim 1, whereinclustering the input medical images into a plurality of clusters basedon one or more of the plurality of metrics to classify the input medicalimages comprises: performing a supervised classification using a randomforest classifier and a logistic regression classifier.
 5. The computerimplemented method of claim 1, further comprising: selecting the one ormore of the plurality of metrics that most discriminate medical imagesassociated with the disease from medical images not associated with thedisease.
 6. The computer implemented method of claim 1, wherein theplurality of metrics for the disease represent the distribution,location, and extent of the disease.
 7. The computer implemented methodof claim 1, wherein the disease is COVID-19 (coronavirus disease 2019).8. An apparatus comprising: means for receiving input medical images;means for computing a plurality of metrics for a disease for each of theinput medical images; and means for clustering the input medical imagesinto a plurality of clusters based on one or more of the plurality ofmetrics to classify the input medical images, the plurality of clusterscomprising: a cluster of one or more of the input medical imagesassociated with the disease, and one or more clusters of one or more ofthe input medical images not associated with the disease.
 9. Theapparatus of claim 8, wherein the means for clustering the input medicalimages into a plurality of clusters based on one or more of theplurality of metrics to classify the input medical images comprises:means for performing unsupervised hierarchical clustering based on adistance between each pair of images in the input medical images. 10.The apparatus of claim 9, further comprising means for computing thedistance between each pair of images in the input medical images by:means for computing an initial distance between same metrics of the oneor more of the plurality of metrics for each respective pair of images;and means for averaging the initial distances between the same metricsfor each respective pair of images.
 11. A non-transitory computerreadable medium storing computer program instructions, the computerprogram instructions when executed by a processor cause the processor toperform operations comprising: receiving input medical images; computinga plurality of metrics for a disease for each of the input medicalimages; and clustering the input medical images into a plurality ofclusters based on one or more of the plurality of metrics to classifythe input medical images, the plurality of clusters comprising: acluster of one or more of the input medical images associated with thedisease, and one or more clusters of one or more of the input medicalimages not associated with the disease.
 12. The non-transitory computerreadable medium of claim 11, wherein clustering the input medical imagesinto a plurality of clusters based on one or more of the plurality ofmetrics to classify the input medical images comprises: performing asupervised classification using a random forest classifier and alogistic regression classifier.
 13. The non-transitory computer readablemedium of claim 11, the operations further comprising: selecting the oneor more of the plurality of metrics that most discriminate medicalimages associated with the disease from medical images not associatedwith the disease.
 14. The non-transitory computer readable medium ofclaim 11, wherein the plurality of metrics for the disease represent thedistribution, location, and extent of the disease.
 15. Thenon-transitory computer readable medium of claim 11, wherein the diseaseis COVID-19 (coronavirus disease 2019).
 16. A computer implementedmethod comprising: receiving an input medical image of lungs of apatient; segmenting the lungs from the input medical image; generating aprobability map for abnormality patterns associated with a disease fromthe input medical image; and determining a classification of the inputmedical image based on the segmented lungs and the probability map, theclassification representing whether the input medical image isassociated with the disease.
 17. The computer implemented method ofclaim 16, wherein the disease is COVID-19 (coronavirus disease 2019) andthe abnormality patterns associated with COVID-19 comprise opacities ofone or more of ground glass opacities (GGO), consolidation, andcrazy-paving pattern.
 18. The computer implemented method of claim 16,wherein the classification of the input medical image is an indicationthat the input medical image is associated with the disease or anindication that the input medical image is not associated with thedisease.
 19. As apparatus comprising: means for receiving an inputmedical image of lungs of a patient; means for segmenting the lungs fromthe input medical image; means for generating a probability map forabnormality patterns associated with a disease from the input medicalimage; and means for determining a classification of the input medicalimage based on the segmented lungs and the probability map, theclassification representing whether the input medical image isassociated with the disease.
 20. The apparatus of claim 19, wherein thedisease is COVID-19 (coronavirus disease 2019) and the abnormalitypatterns associated with COVID-19 comprise opacities of one or more ofground glass opacities (GGO), consolidation, and crazy-paving pattern.21. The apparatus of claim 19, wherein the classification of the inputmedical image is an indication that the input medical image isassociated with the disease or an indication that the input medicalimage is not associated with the disease.
 22. A non-transitory computerreadable medium storing computer program instructions, the computerprogram instructions when executed by a processor cause the processor toperform operations comprising: receiving an input medical image of lungsof a patient; segmenting the lungs from the input medical image;generating a probability map for abnormality patterns associated with adisease from the input medical image; and determining a classificationof the input medical image based on the segmented lungs and theprobability map, the classification representing whether the inputmedical image is associated with the disease.
 23. The non-transitorycomputer readable medium of claim 22, wherein the disease is COVID-19(coronavirus disease 2019) and the abnormality patterns associated withCOVID-19 comprise opacities of one or more of ground glass opacities(GGO), consolidation, and crazy-paving pattern.
 24. The non-transitorycomputer readable medium of claim 22, wherein the classification of theinput medical image is an indication that the input medical image isassociated with the disease or an indication that the input medicalimage is not associated with the disease.